Overview

Dataset statistics

Number of variables40
Number of observations1000
Missing cells1000
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory312.6 KiB
Average record size in memory320.1 B

Variable types

Numeric14
Categorical24
Boolean1
Unsupported1

Alerts

policy_bind_date has a high cardinality: 951 distinct valuesHigh cardinality
incident_date has a high cardinality: 60 distinct valuesHigh cardinality
incident_location has a high cardinality: 1000 distinct valuesHigh cardinality
months_as_customer is highly overall correlated with ageHigh correlation
age is highly overall correlated with months_as_customerHigh correlation
total_claim_amount is highly overall correlated with injury_claim and 4 other fieldsHigh correlation
injury_claim is highly overall correlated with total_claim_amount and 4 other fieldsHigh correlation
property_claim is highly overall correlated with total_claim_amount and 4 other fieldsHigh correlation
vehicle_claim is highly overall correlated with total_claim_amount and 4 other fieldsHigh correlation
incident_type is highly overall correlated with total_claim_amount and 5 other fieldsHigh correlation
collision_type is highly overall correlated with total_claim_amount and 4 other fieldsHigh correlation
incident_severity is highly overall correlated with fraud_reportedHigh correlation
number_of_vehicles_involved is highly overall correlated with incident_typeHigh correlation
auto_make is highly overall correlated with auto_modelHigh correlation
auto_model is highly overall correlated with auto_makeHigh correlation
fraud_reported is highly overall correlated with incident_severityHigh correlation
_c39 has 1000 (100.0%) missing valuesMissing
policy_bind_date is uniformly distributedUniform
incident_location is uniformly distributedUniform
policy_number has unique valuesUnique
incident_location has unique valuesUnique
_c39 is an unsupported type, check if it needs cleaning or further analysisUnsupported
umbrella_limit has 798 (79.8%) zerosZeros
capital-gains has 508 (50.8%) zerosZeros
capital-loss has 475 (47.5%) zerosZeros
incident_hour_of_the_day has 52 (5.2%) zerosZeros
injury_claim has 25 (2.5%) zerosZeros
property_claim has 19 (1.9%) zerosZeros

Reproduction

Analysis started2023-08-08 00:21:05.886660
Analysis finished2023-08-08 00:21:29.651748
Duration23.77 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

months_as_customer
Real number (ℝ)

Distinct391
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.954
Minimum0
Maximum479
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:29.723518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.9
Q1115.75
median199.5
Q3276.25
95-th percentile429.05
Maximum479
Range479
Interquartile range (IQR)160.5

Descriptive statistics

Standard deviation115.11317
Coefficient of variation (CV)0.56440754
Kurtosis-0.48542807
Mean203.954
Median Absolute Deviation (MAD)80.5
Skewness0.36217685
Sum203954
Variance13251.043
MonotonicityNot monotonic
2023-08-07T20:21:29.828735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194 8
 
0.8%
128 7
 
0.7%
254 7
 
0.7%
140 7
 
0.7%
210 7
 
0.7%
230 7
 
0.7%
285 7
 
0.7%
101 7
 
0.7%
239 6
 
0.6%
126 6
 
0.6%
Other values (381) 931
93.1%
ValueCountFrequency (%)
0 1
 
0.1%
1 3
0.3%
2 2
0.2%
3 2
0.2%
4 3
0.3%
5 2
0.2%
6 1
 
0.1%
7 1
 
0.1%
8 3
0.3%
9 2
0.2%
ValueCountFrequency (%)
479 2
0.2%
478 2
0.2%
476 1
0.1%
475 2
0.2%
473 1
0.1%
472 1
0.1%
468 1
0.1%
467 1
0.1%
465 1
0.1%
464 1
0.1%

age
Real number (ℝ)

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.948
Minimum19
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:29.928083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile26
Q132
median38
Q344
95-th percentile57
Maximum64
Range45
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.1402867
Coefficient of variation (CV)0.23467923
Kurtosis-0.26025502
Mean38.948
Median Absolute Deviation (MAD)6
Skewness0.47898805
Sum38948
Variance83.544841
MonotonicityNot monotonic
2023-08-07T20:21:30.021082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
43 49
 
4.9%
39 48
 
4.8%
41 45
 
4.5%
34 44
 
4.4%
38 42
 
4.2%
30 42
 
4.2%
31 42
 
4.2%
37 41
 
4.1%
33 39
 
3.9%
40 38
 
3.8%
Other values (36) 570
57.0%
ValueCountFrequency (%)
19 1
 
0.1%
20 1
 
0.1%
21 6
 
0.6%
22 1
 
0.1%
23 7
 
0.7%
24 10
 
1.0%
25 14
1.4%
26 26
2.6%
27 24
2.4%
28 30
3.0%
ValueCountFrequency (%)
64 2
 
0.2%
63 2
 
0.2%
62 4
 
0.4%
61 10
1.0%
60 9
0.9%
59 5
 
0.5%
58 8
0.8%
57 16
1.6%
56 8
0.8%
55 14
1.4%

policy_number
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546238.65
Minimum100804
Maximum999435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:30.118963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum100804
5-th percentile143969.6
Q1335980.25
median533135
Q3759099.75
95-th percentile954279.1
Maximum999435
Range898631
Interquartile range (IQR)423119.5

Descriptive statistics

Standard deviation257063.01
Coefficient of variation (CV)0.47060567
Kurtosis-1.1326377
Mean546238.65
Median Absolute Deviation (MAD)210974
Skewness0.038990642
Sum5.4623865 × 108
Variance6.6081389 × 1010
MonotonicityNot monotonic
2023-08-07T20:21:30.217161image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521585 1
 
0.1%
687755 1
 
0.1%
674485 1
 
0.1%
223404 1
 
0.1%
991480 1
 
0.1%
804219 1
 
0.1%
483088 1
 
0.1%
100804 1
 
0.1%
941807 1
 
0.1%
593466 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
100804 1
0.1%
101421 1
0.1%
104594 1
0.1%
106186 1
0.1%
106873 1
0.1%
107181 1
0.1%
108270 1
0.1%
108844 1
0.1%
109392 1
0.1%
110084 1
0.1%
ValueCountFrequency (%)
999435 1
0.1%
998865 1
0.1%
998192 1
0.1%
996850 1
0.1%
996253 1
0.1%
994538 1
0.1%
993840 1
0.1%
992145 1
0.1%
991553 1
0.1%
991480 1
0.1%

policy_bind_date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct951
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2006-01-01
 
3
1992-04-28
 
3
1992-08-05
 
3
1991-12-14
 
2
2004-08-09
 
2
Other values (946)
987 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique905 ?
Unique (%)90.5%

Sample

1st row2014-10-17
2nd row2006-06-27
3rd row2000-09-06
4th row1990-05-25
5th row2014-06-06

Common Values

ValueCountFrequency (%)
2006-01-01 3
 
0.3%
1992-04-28 3
 
0.3%
1992-08-05 3
 
0.3%
1991-12-14 2
 
0.2%
2004-08-09 2
 
0.2%
2010-01-28 2
 
0.2%
1999-09-29 2
 
0.2%
2001-09-25 2
 
0.2%
2000-05-04 2
 
0.2%
1997-02-03 2
 
0.2%
Other values (941) 977
97.7%

Length

2023-08-07T20:21:30.308633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2006-01-01 3
 
0.3%
1992-08-05 3
 
0.3%
1992-04-28 3
 
0.3%
1996-09-21 2
 
0.2%
2014-07-05 2
 
0.2%
1997-05-15 2
 
0.2%
1992-04-14 2
 
0.2%
2003-03-09 2
 
0.2%
1997-11-07 2
 
0.2%
1992-01-05 2
 
0.2%
Other values (941) 977
97.7%

Most occurring characters

ValueCountFrequency (%)
0 2315
23.2%
- 2000
20.0%
1 1609
16.1%
2 1298
13.0%
9 1105
11.1%
3 308
 
3.1%
4 299
 
3.0%
7 275
 
2.8%
8 274
 
2.7%
5 263
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8000
80.0%
Dash Punctuation 2000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2315
28.9%
1 1609
20.1%
2 1298
16.2%
9 1105
13.8%
3 308
 
3.9%
4 299
 
3.7%
7 275
 
3.4%
8 274
 
3.4%
5 263
 
3.3%
6 254
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2315
23.2%
- 2000
20.0%
1 1609
16.1%
2 1298
13.0%
9 1105
11.1%
3 308
 
3.1%
4 299
 
3.0%
7 275
 
2.8%
8 274
 
2.7%
5 263
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2315
23.2%
- 2000
20.0%
1 1609
16.1%
2 1298
13.0%
9 1105
11.1%
3 308
 
3.1%
4 299
 
3.0%
7 275
 
2.8%
8 274
 
2.7%
5 263
 
2.6%

policy_state
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
OH
352 
IL
338 
IN
310 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowIN
3rd rowOH
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
OH 352
35.2%
IL 338
33.8%
IN 310
31.0%

Length

2023-08-07T20:21:30.384301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:30.474983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
oh 352
35.2%
il 338
33.8%
in 310
31.0%

Most occurring characters

ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

policy_csl
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
250/500
351 
100/300
349 
500/1000
300 

Length

Max length8
Median length7
Mean length7.3
Min length7

Characters and Unicode

Total characters7300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row250/500
2nd row250/500
3rd row100/300
4th row250/500
5th row500/1000

Common Values

ValueCountFrequency (%)
250/500 351
35.1%
100/300 349
34.9%
500/1000 300
30.0%

Length

2023-08-07T20:21:30.552918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:30.780971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
250/500 351
35.1%
100/300 349
34.9%
500/1000 300
30.0%

Most occurring characters

ValueCountFrequency (%)
0 3949
54.1%
5 1002
 
13.7%
/ 1000
 
13.7%
1 649
 
8.9%
2 351
 
4.8%
3 349
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6300
86.3%
Other Punctuation 1000
 
13.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3949
62.7%
5 1002
 
15.9%
1 649
 
10.3%
2 351
 
5.6%
3 349
 
5.5%
Other Punctuation
ValueCountFrequency (%)
/ 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3949
54.1%
5 1002
 
13.7%
/ 1000
 
13.7%
1 649
 
8.9%
2 351
 
4.8%
3 349
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3949
54.1%
5 1002
 
13.7%
/ 1000
 
13.7%
1 649
 
8.9%
2 351
 
4.8%
3 349
 
4.8%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1000
351 
500
342 
2000
307 

Length

Max length4
Median length4
Mean length3.658
Min length3

Characters and Unicode

Total characters3658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000
2nd row2000
3rd row2000
4th row2000
5th row1000

Common Values

ValueCountFrequency (%)
1000 351
35.1%
500 342
34.2%
2000 307
30.7%

Length

2023-08-07T20:21:30.858481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:30.944997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1000 351
35.1%
500 342
34.2%
2000 307
30.7%

Most occurring characters

ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

policy_annual_premium
Real number (ℝ)

Distinct991
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1256.4061
Minimum433.33
Maximum2047.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:31.029512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum433.33
5-th percentile855.112
Q11089.6075
median1257.2
Q31415.695
95-th percentile1653.4435
Maximum2047.59
Range1614.26
Interquartile range (IQR)326.0875

Descriptive statistics

Standard deviation244.16739
Coefficient of variation (CV)0.19433795
Kurtosis0.07388944
Mean1256.4061
Median Absolute Deviation (MAD)164.26
Skewness0.0044019945
Sum1256406.2
Variance59617.717
MonotonicityNot monotonic
2023-08-07T20:21:31.133031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1558.29 2
 
0.2%
1215.36 2
 
0.2%
1362.87 2
 
0.2%
1073.83 2
 
0.2%
1389.13 2
 
0.2%
1074.07 2
 
0.2%
1374.22 2
 
0.2%
1524.45 2
 
0.2%
1281.25 2
 
0.2%
1230.69 1
 
0.1%
Other values (981) 981
98.1%
ValueCountFrequency (%)
433.33 1
0.1%
484.67 1
0.1%
538.17 1
0.1%
566.11 1
0.1%
617.11 1
0.1%
625.08 1
0.1%
653.66 1
0.1%
664.86 1
0.1%
671.01 1
0.1%
671.92 1
0.1%
ValueCountFrequency (%)
2047.59 1
0.1%
1969.63 1
0.1%
1935.85 1
0.1%
1927.87 1
0.1%
1922.84 1
0.1%
1896.91 1
0.1%
1878.44 1
0.1%
1865.83 1
0.1%
1863.04 1
0.1%
1861.43 1
0.1%

umbrella_limit
Real number (ℝ)

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1101000
Minimum-1000000
Maximum10000000
Zeros798
Zeros (%)79.8%
Negative1
Negative (%)0.1%
Memory size7.9 KiB
2023-08-07T20:21:31.232385image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-1000000
5-th percentile0
Q10
median0
Q30
95-th percentile6000000
Maximum10000000
Range11000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2297406.6
Coefficient of variation (CV)2.0866545
Kurtosis1.7920773
Mean1101000
Median Absolute Deviation (MAD)0
Skewness1.8067122
Sum1.101 × 109
Variance5.2780771 × 1012
MonotonicityNot monotonic
2023-08-07T20:21:31.303932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 798
79.8%
6000000 57
 
5.7%
5000000 46
 
4.6%
4000000 39
 
3.9%
7000000 29
 
2.9%
3000000 12
 
1.2%
8000000 8
 
0.8%
9000000 5
 
0.5%
2000000 3
 
0.3%
10000000 2
 
0.2%
ValueCountFrequency (%)
-1000000 1
 
0.1%
0 798
79.8%
2000000 3
 
0.3%
3000000 12
 
1.2%
4000000 39
 
3.9%
5000000 46
 
4.6%
6000000 57
 
5.7%
7000000 29
 
2.9%
8000000 8
 
0.8%
9000000 5
 
0.5%
ValueCountFrequency (%)
10000000 2
 
0.2%
9000000 5
 
0.5%
8000000 8
 
0.8%
7000000 29
 
2.9%
6000000 57
 
5.7%
5000000 46
 
4.6%
4000000 39
 
3.9%
3000000 12
 
1.2%
2000000 3
 
0.3%
0 798
79.8%

insured_zip
Real number (ℝ)

Distinct995
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501214.49
Minimum430104
Maximum620962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:31.392838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum430104
5-th percentile433273.75
Q1448404.5
median466445.5
Q3603251
95-th percentile617463.35
Maximum620962
Range190858
Interquartile range (IQR)154846.5

Descriptive statistics

Standard deviation71701.611
Coefficient of variation (CV)0.14305574
Kurtosis-1.1907111
Mean501214.49
Median Absolute Deviation (MAD)21841
Skewness0.81655393
Sum5.0121449 × 108
Variance5.141121 × 109
MonotonicityNot monotonic
2023-08-07T20:21:31.491706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
477695 2
 
0.2%
469429 2
 
0.2%
446895 2
 
0.2%
431202 2
 
0.2%
456602 2
 
0.2%
466132 1
 
0.1%
452218 1
 
0.1%
608982 1
 
0.1%
459630 1
 
0.1%
453193 1
 
0.1%
Other values (985) 985
98.5%
ValueCountFrequency (%)
430104 1
0.1%
430141 1
0.1%
430232 1
0.1%
430380 1
0.1%
430567 1
0.1%
430621 1
0.1%
430632 1
0.1%
430665 1
0.1%
430714 1
0.1%
430832 1
0.1%
ValueCountFrequency (%)
620962 1
0.1%
620869 1
0.1%
620819 1
0.1%
620757 1
0.1%
620737 1
0.1%
620507 1
0.1%
620493 1
0.1%
620473 1
0.1%
620358 1
0.1%
620207 1
0.1%

insured_sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
FEMALE
537 
MALE
463 

Length

Max length6
Median length6
Mean length5.074
Min length4

Characters and Unicode

Total characters5074
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
FEMALE 537
53.7%
MALE 463
46.3%

Length

2023-08-07T20:21:31.594426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:31.681354image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
female 537
53.7%
male 463
46.3%

Most occurring characters

ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5074
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%
Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
JD
161 
High School
160 
Associate
145 
MD
144 
Masters
143 
Other values (2)
247 

Length

Max length11
Median length9
Mean length5.905
Min length2

Characters and Unicode

Total characters5905
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMD
2nd rowMD
3rd rowPhD
4th rowPhD
5th rowAssociate

Common Values

ValueCountFrequency (%)
JD 161
16.1%
High School 160
16.0%
Associate 145
14.5%
MD 144
14.4%
Masters 143
14.3%
PhD 125
12.5%
College 122
12.2%

Length

2023-08-07T20:21:31.749126image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:31.839935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
jd 161
13.9%
high 160
13.8%
school 160
13.8%
associate 145
12.5%
md 144
12.4%
masters 143
12.3%
phd 125
10.8%
college 122
10.5%

Most occurring characters

ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4155
70.4%
Uppercase Letter 1590
 
26.9%
Space Separator 160
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 587
14.1%
s 576
13.9%
e 532
12.8%
h 445
10.7%
l 404
9.7%
i 305
7.3%
c 305
7.3%
t 288
6.9%
a 288
6.9%
g 282
6.8%
Uppercase Letter
ValueCountFrequency (%)
D 430
27.0%
M 287
18.1%
J 161
 
10.1%
S 160
 
10.1%
H 160
 
10.1%
A 145
 
9.1%
P 125
 
7.9%
C 122
 
7.7%
Space Separator
ValueCountFrequency (%)
160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5745
97.3%
Common 160
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 587
 
10.2%
s 576
 
10.0%
e 532
 
9.3%
h 445
 
7.7%
D 430
 
7.5%
l 404
 
7.0%
i 305
 
5.3%
c 305
 
5.3%
t 288
 
5.0%
a 288
 
5.0%
Other values (9) 1585
27.6%
Common
ValueCountFrequency (%)
160
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%
Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
machine-op-inspct
93 
prof-specialty
85 
tech-support
78 
sales
76 
exec-managerial
76 
Other values (9)
592 

Length

Max length17
Median length16
Mean length13.521
Min length5

Characters and Unicode

Total characters13521
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcraft-repair
2nd rowmachine-op-inspct
3rd rowsales
4th rowarmed-forces
5th rowsales

Common Values

ValueCountFrequency (%)
machine-op-inspct 93
 
9.3%
prof-specialty 85
 
8.5%
tech-support 78
 
7.8%
sales 76
 
7.6%
exec-managerial 76
 
7.6%
craft-repair 74
 
7.4%
transport-moving 72
 
7.2%
other-service 71
 
7.1%
priv-house-serv 71
 
7.1%
armed-forces 69
 
6.9%
Other values (4) 235
23.5%

Length

2023-08-07T20:21:31.941768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
machine-op-inspct 93
 
9.3%
prof-specialty 85
 
8.5%
tech-support 78
 
7.8%
sales 76
 
7.6%
exec-managerial 76
 
7.6%
craft-repair 74
 
7.4%
transport-moving 72
 
7.2%
other-service 71
 
7.1%
priv-house-serv 71
 
7.1%
armed-forces 69
 
6.9%
Other values (4) 235
23.5%

Most occurring characters

ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12433
92.0%
Dash Punctuation 1088
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1543
12.4%
r 1379
11.1%
a 1062
 
8.5%
s 986
 
7.9%
i 922
 
7.4%
c 886
 
7.1%
p 792
 
6.4%
t 749
 
6.0%
o 674
 
5.4%
n 620
 
5.0%
Other values (10) 2820
22.7%
Dash Punctuation
ValueCountFrequency (%)
- 1088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12433
92.0%
Common 1088
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1543
12.4%
r 1379
11.1%
a 1062
 
8.5%
s 986
 
7.9%
i 922
 
7.4%
c 886
 
7.1%
p 792
 
6.4%
t 749
 
6.0%
o 674
 
5.4%
n 620
 
5.0%
Other values (10) 2820
22.7%
Common
ValueCountFrequency (%)
- 1088
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%

insured_hobbies
Categorical

Distinct20
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
reading
 
64
exercise
 
57
paintball
 
57
bungie-jumping
 
56
movies
 
55
Other values (15)
711 

Length

Max length14
Median length11
Mean length8.113
Min length4

Characters and Unicode

Total characters8113
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsleeping
2nd rowreading
3rd rowboard-games
4th rowboard-games
5th rowboard-games

Common Values

ValueCountFrequency (%)
reading 64
 
6.4%
exercise 57
 
5.7%
paintball 57
 
5.7%
bungie-jumping 56
 
5.6%
movies 55
 
5.5%
golf 55
 
5.5%
camping 55
 
5.5%
kayaking 54
 
5.4%
yachting 53
 
5.3%
hiking 52
 
5.2%
Other values (10) 442
44.2%

Length

2023-08-07T20:21:32.029204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
reading 64
 
6.4%
exercise 57
 
5.7%
paintball 57
 
5.7%
bungie-jumping 56
 
5.6%
movies 55
 
5.5%
golf 55
 
5.5%
camping 55
 
5.5%
kayaking 54
 
5.4%
yachting 53
 
5.3%
hiking 52
 
5.2%
Other values (10) 442
44.2%

Most occurring characters

ValueCountFrequency (%)
i 927
 
11.4%
g 725
 
8.9%
e 705
 
8.7%
a 700
 
8.6%
n 672
 
8.3%
s 545
 
6.7%
o 337
 
4.2%
l 325
 
4.0%
m 313
 
3.9%
p 305
 
3.8%
Other values (14) 2559
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7875
97.1%
Dash Punctuation 238
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 927
 
11.8%
g 725
 
9.2%
e 705
 
9.0%
a 700
 
8.9%
n 672
 
8.5%
s 545
 
6.9%
o 337
 
4.3%
l 325
 
4.1%
m 313
 
4.0%
p 305
 
3.9%
Other values (13) 2321
29.5%
Dash Punctuation
ValueCountFrequency (%)
- 238
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7875
97.1%
Common 238
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 927
 
11.8%
g 725
 
9.2%
e 705
 
9.0%
a 700
 
8.9%
n 672
 
8.5%
s 545
 
6.9%
o 337
 
4.3%
l 325
 
4.1%
m 313
 
4.0%
p 305
 
3.9%
Other values (13) 2321
29.5%
Common
ValueCountFrequency (%)
- 238
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 927
 
11.4%
g 725
 
8.9%
e 705
 
8.7%
a 700
 
8.6%
n 672
 
8.3%
s 545
 
6.7%
o 337
 
4.2%
l 325
 
4.0%
m 313
 
3.9%
p 305
 
3.8%
Other values (14) 2559
31.5%
Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
own-child
183 
other-relative
177 
not-in-family
174 
husband
170 
wife
155 

Length

Max length14
Median length13
Mean length9.466
Min length4

Characters and Unicode

Total characters9466
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhusband
2nd rowother-relative
3rd rowown-child
4th rowunmarried
5th rowunmarried

Common Values

ValueCountFrequency (%)
own-child 183
18.3%
other-relative 177
17.7%
not-in-family 174
17.4%
husband 170
17.0%
wife 155
15.5%
unmarried 141
14.1%

Length

2023-08-07T20:21:32.115398image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:32.214972image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
own-child 183
18.3%
other-relative 177
17.7%
not-in-family 174
17.4%
husband 170
17.0%
wife 155
15.5%
unmarried 141
14.1%

Most occurring characters

ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8758
92.5%
Dash Punctuation 708
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1004
11.5%
n 842
 
9.6%
e 827
 
9.4%
a 662
 
7.6%
r 636
 
7.3%
l 534
 
6.1%
o 534
 
6.1%
h 530
 
6.1%
t 528
 
6.0%
d 494
 
5.6%
Other values (9) 2167
24.7%
Dash Punctuation
ValueCountFrequency (%)
- 708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8758
92.5%
Common 708
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1004
11.5%
n 842
 
9.6%
e 827
 
9.4%
a 662
 
7.6%
r 636
 
7.3%
l 534
 
6.1%
o 534
 
6.1%
h 530
 
6.1%
t 528
 
6.0%
d 494
 
5.6%
Other values (9) 2167
24.7%
Common
ValueCountFrequency (%)
- 708
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

capital-gains
Real number (ℝ)

Distinct338
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25126.1
Minimum0
Maximum100500
Zeros508
Zeros (%)50.8%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:32.316708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q351025
95-th percentile70615
Maximum100500
Range100500
Interquartile range (IQR)51025

Descriptive statistics

Standard deviation27872.188
Coefficient of variation (CV)1.1092922
Kurtosis-1.2767035
Mean25126.1
Median Absolute Deviation (MAD)0
Skewness0.47885023
Sum25126100
Variance7.7685885 × 108
MonotonicityNot monotonic
2023-08-07T20:21:32.415502image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 508
50.8%
46300 5
 
0.5%
51500 4
 
0.4%
68500 4
 
0.4%
55600 3
 
0.3%
49700 3
 
0.3%
51700 3
 
0.3%
56700 3
 
0.3%
47600 3
 
0.3%
44000 3
 
0.3%
Other values (328) 461
46.1%
ValueCountFrequency (%)
0 508
50.8%
800 1
 
0.1%
10000 1
 
0.1%
11000 1
 
0.1%
12100 1
 
0.1%
12800 1
 
0.1%
13100 1
 
0.1%
14100 1
 
0.1%
16100 1
 
0.1%
17300 1
 
0.1%
ValueCountFrequency (%)
100500 1
0.1%
98800 1
0.1%
94800 1
0.1%
91900 1
0.1%
90700 1
0.1%
88800 1
0.1%
88400 1
0.1%
87800 1
0.1%
84900 1
0.1%
83900 1
0.1%

capital-loss
Real number (ℝ)

Distinct354
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26793.7
Minimum-111100
Maximum0
Zeros475
Zeros (%)47.5%
Negative525
Negative (%)52.5%
Memory size7.9 KiB
2023-08-07T20:21:32.515340image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-111100
5-th percentile-72305
Q1-51500
median-23250
Q30
95-th percentile0
Maximum0
Range111100
Interquartile range (IQR)51500

Descriptive statistics

Standard deviation28104.097
Coefficient of variation (CV)-1.0489069
Kurtosis-1.3138745
Mean-26793.7
Median Absolute Deviation (MAD)23250
Skewness-0.39147194
Sum-26793700
Variance7.8984025 × 108
MonotonicityNot monotonic
2023-08-07T20:21:32.609992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
47.5%
-31700 5
 
0.5%
-53700 5
 
0.5%
-50300 5
 
0.5%
-45300 4
 
0.4%
-51000 4
 
0.4%
-32800 4
 
0.4%
-53800 4
 
0.4%
-49200 4
 
0.4%
-31400 4
 
0.4%
Other values (344) 486
48.6%
ValueCountFrequency (%)
-111100 1
0.1%
-93600 1
0.1%
-91400 1
0.1%
-91200 1
0.1%
-90600 1
0.1%
-90200 1
0.1%
-90100 1
0.1%
-89400 1
0.1%
-88300 1
0.1%
-87300 1
0.1%
ValueCountFrequency (%)
0 475
47.5%
-5700 1
 
0.1%
-6300 1
 
0.1%
-8500 1
 
0.1%
-10600 1
 
0.1%
-12100 1
 
0.1%
-13200 1
 
0.1%
-13800 1
 
0.1%
-15600 1
 
0.1%
-15700 2
 
0.2%

incident_date
Categorical

Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2015-02-02
 
28
2015-02-17
 
26
2015-01-07
 
25
2015-01-10
 
24
2015-02-04
 
24
Other values (55)
873 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-01-25
2nd row2015-01-21
3rd row2015-02-22
4th row2015-01-10
5th row2015-02-17

Common Values

ValueCountFrequency (%)
2015-02-02 28
 
2.8%
2015-02-17 26
 
2.6%
2015-01-07 25
 
2.5%
2015-01-10 24
 
2.4%
2015-02-04 24
 
2.4%
2015-01-24 24
 
2.4%
2015-01-19 23
 
2.3%
2015-01-08 22
 
2.2%
2015-01-13 21
 
2.1%
2015-01-30 21
 
2.1%
Other values (50) 762
76.2%

Length

2023-08-07T20:21:32.697887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-02-02 28
 
2.8%
2015-02-17 26
 
2.6%
2015-01-07 25
 
2.5%
2015-02-04 24
 
2.4%
2015-01-24 24
 
2.4%
2015-01-10 24
 
2.4%
2015-01-19 23
 
2.3%
2015-01-08 22
 
2.2%
2015-01-13 21
 
2.1%
2015-01-30 21
 
2.1%
Other values (50) 762
76.2%

Most occurring characters

ValueCountFrequency (%)
0 2406
24.1%
- 2000
20.0%
1 1978
19.8%
2 1888
18.9%
5 1082
10.8%
3 153
 
1.5%
4 114
 
1.1%
8 105
 
1.1%
7 103
 
1.0%
6 97
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8000
80.0%
Dash Punctuation 2000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2406
30.1%
1 1978
24.7%
2 1888
23.6%
5 1082
13.5%
3 153
 
1.9%
4 114
 
1.4%
8 105
 
1.3%
7 103
 
1.3%
6 97
 
1.2%
9 74
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2406
24.1%
- 2000
20.0%
1 1978
19.8%
2 1888
18.9%
5 1082
10.8%
3 153
 
1.5%
4 114
 
1.1%
8 105
 
1.1%
7 103
 
1.0%
6 97
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2406
24.1%
- 2000
20.0%
1 1978
19.8%
2 1888
18.9%
5 1082
10.8%
3 153
 
1.5%
4 114
 
1.1%
8 105
 
1.1%
7 103
 
1.0%
6 97
 
1.0%

incident_type
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Multi-vehicle Collision
419 
Single Vehicle Collision
403 
Vehicle Theft
94 
Parked Car
84 

Length

Max length24
Median length23
Mean length21.371
Min length10

Characters and Unicode

Total characters21371
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle Vehicle Collision
2nd rowVehicle Theft
3rd rowMulti-vehicle Collision
4th rowSingle Vehicle Collision
5th rowVehicle Theft

Common Values

ValueCountFrequency (%)
Multi-vehicle Collision 419
41.9%
Single Vehicle Collision 403
40.3%
Vehicle Theft 94
 
9.4%
Parked Car 84
 
8.4%

Length

2023-08-07T20:21:32.778698image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:32.881533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
collision 822
34.2%
vehicle 497
20.7%
multi-vehicle 419
17.4%
single 403
16.8%
theft 94
 
3.9%
parked 84
 
3.5%
car 84
 
3.5%

Most occurring characters

ValueCountFrequency (%)
l 3382
15.8%
i 3382
15.8%
e 2413
11.3%
o 1644
 
7.7%
1403
 
6.6%
n 1225
 
5.7%
h 1010
 
4.7%
c 916
 
4.3%
C 906
 
4.2%
s 822
 
3.8%
Other values (15) 4268
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17146
80.2%
Uppercase Letter 2403
 
11.2%
Space Separator 1403
 
6.6%
Dash Punctuation 419
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 3382
19.7%
i 3382
19.7%
e 2413
14.1%
o 1644
9.6%
n 1225
 
7.1%
h 1010
 
5.9%
c 916
 
5.3%
s 822
 
4.8%
t 513
 
3.0%
u 419
 
2.4%
Other values (7) 1420
8.3%
Uppercase Letter
ValueCountFrequency (%)
C 906
37.7%
V 497
20.7%
M 419
17.4%
S 403
16.8%
T 94
 
3.9%
P 84
 
3.5%
Space Separator
ValueCountFrequency (%)
1403
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19549
91.5%
Common 1822
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 3382
17.3%
i 3382
17.3%
e 2413
12.3%
o 1644
8.4%
n 1225
 
6.3%
h 1010
 
5.2%
c 916
 
4.7%
C 906
 
4.6%
s 822
 
4.2%
t 513
 
2.6%
Other values (13) 3336
17.1%
Common
ValueCountFrequency (%)
1403
77.0%
- 419
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 3382
15.8%
i 3382
15.8%
e 2413
11.3%
o 1644
 
7.7%
1403
 
6.6%
n 1225
 
5.7%
h 1010
 
4.7%
c 916
 
4.3%
C 906
 
4.2%
s 822
 
3.8%
Other values (15) 4268
20.0%

collision_type
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Rear Collision
292 
Side Collision
276 
Front Collision
254 
?
178 

Length

Max length15
Median length14
Mean length11.94
Min length1

Characters and Unicode

Total characters11940
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSide Collision
2nd row?
3rd rowRear Collision
4th rowFront Collision
5th row?

Common Values

ValueCountFrequency (%)
Rear Collision 292
29.2%
Side Collision 276
27.6%
Front Collision 254
25.4%
? 178
17.8%

Length

2023-08-07T20:21:32.966240image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:33.054012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
collision 822
45.1%
rear 292
 
16.0%
side 276
 
15.1%
front 254
 
13.9%
178
 
9.8%

Most occurring characters

ValueCountFrequency (%)
i 1920
16.1%
o 1898
15.9%
l 1644
13.8%
n 1076
9.0%
822
6.9%
C 822
6.9%
s 822
6.9%
e 568
 
4.8%
r 546
 
4.6%
R 292
 
2.4%
Other values (6) 1530
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9296
77.9%
Uppercase Letter 1644
 
13.8%
Space Separator 822
 
6.9%
Other Punctuation 178
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1920
20.7%
o 1898
20.4%
l 1644
17.7%
n 1076
11.6%
s 822
8.8%
e 568
 
6.1%
r 546
 
5.9%
a 292
 
3.1%
d 276
 
3.0%
t 254
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 822
50.0%
R 292
 
17.8%
S 276
 
16.8%
F 254
 
15.5%
Space Separator
ValueCountFrequency (%)
822
100.0%
Other Punctuation
ValueCountFrequency (%)
? 178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10940
91.6%
Common 1000
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1920
17.6%
o 1898
17.3%
l 1644
15.0%
n 1076
9.8%
C 822
7.5%
s 822
7.5%
e 568
 
5.2%
r 546
 
5.0%
R 292
 
2.7%
a 292
 
2.7%
Other values (4) 1060
9.7%
Common
ValueCountFrequency (%)
822
82.2%
? 178
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1920
16.1%
o 1898
15.9%
l 1644
13.8%
n 1076
9.0%
822
6.9%
C 822
6.9%
s 822
6.9%
e 568
 
4.8%
r 546
 
4.6%
R 292
 
2.4%
Other values (6) 1530
12.8%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minor Damage
354 
Total Loss
280 
Major Damage
276 
Trivial Damage
90 

Length

Max length14
Median length12
Mean length11.62
Min length10

Characters and Unicode

Total characters11620
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor Damage
2nd rowMinor Damage
3rd rowMinor Damage
4th rowMajor Damage
5th rowMinor Damage

Common Values

ValueCountFrequency (%)
Minor Damage 354
35.4%
Total Loss 280
28.0%
Major Damage 276
27.6%
Trivial Damage 90
 
9.0%

Length

2023-08-07T20:21:33.144841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:33.242554image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
damage 720
36.0%
minor 354
17.7%
total 280
 
14.0%
loss 280
 
14.0%
major 276
 
13.8%
trivial 90
 
4.5%

Most occurring characters

ValueCountFrequency (%)
a 2086
18.0%
o 1190
10.2%
1000
 
8.6%
g 720
 
6.2%
m 720
 
6.2%
e 720
 
6.2%
r 720
 
6.2%
D 720
 
6.2%
M 630
 
5.4%
s 560
 
4.8%
Other values (8) 2554
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8620
74.2%
Uppercase Letter 2000
 
17.2%
Space Separator 1000
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2086
24.2%
o 1190
13.8%
g 720
 
8.4%
m 720
 
8.4%
e 720
 
8.4%
r 720
 
8.4%
s 560
 
6.5%
i 534
 
6.2%
l 370
 
4.3%
n 354
 
4.1%
Other values (3) 646
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
D 720
36.0%
M 630
31.5%
T 370
18.5%
L 280
 
14.0%
Space Separator
ValueCountFrequency (%)
1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10620
91.4%
Common 1000
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2086
19.6%
o 1190
11.2%
g 720
 
6.8%
m 720
 
6.8%
e 720
 
6.8%
r 720
 
6.8%
D 720
 
6.8%
M 630
 
5.9%
s 560
 
5.3%
i 534
 
5.0%
Other values (7) 2020
19.0%
Common
ValueCountFrequency (%)
1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2086
18.0%
o 1190
10.2%
1000
 
8.6%
g 720
 
6.2%
m 720
 
6.2%
e 720
 
6.2%
r 720
 
6.2%
D 720
 
6.2%
M 630
 
5.4%
s 560
 
4.8%
Other values (8) 2554
22.0%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Police
292 
Fire
223 
Other
198 
Ambulance
196 
None
91 

Length

Max length9
Median length6
Mean length5.762
Min length4

Characters and Unicode

Total characters5762
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPolice
2nd rowPolice
3rd rowPolice
4th rowPolice
5th rowNone

Common Values

ValueCountFrequency (%)
Police 292
29.2%
Fire 223
22.3%
Other 198
19.8%
Ambulance 196
19.6%
None 91
 
9.1%

Length

2023-08-07T20:21:33.326462image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:33.415160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
police 292
29.2%
fire 223
22.3%
other 198
19.8%
ambulance 196
19.6%
none 91
 
9.1%

Most occurring characters

ValueCountFrequency (%)
e 1000
17.4%
i 515
 
8.9%
l 488
 
8.5%
c 488
 
8.5%
r 421
 
7.3%
o 383
 
6.6%
P 292
 
5.1%
n 287
 
5.0%
F 223
 
3.9%
h 198
 
3.4%
Other values (8) 1467
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4762
82.6%
Uppercase Letter 1000
 
17.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1000
21.0%
i 515
10.8%
l 488
10.2%
c 488
10.2%
r 421
8.8%
o 383
 
8.0%
n 287
 
6.0%
h 198
 
4.2%
t 198
 
4.2%
m 196
 
4.1%
Other values (3) 588
12.3%
Uppercase Letter
ValueCountFrequency (%)
P 292
29.2%
F 223
22.3%
O 198
19.8%
A 196
19.6%
N 91
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5762
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1000
17.4%
i 515
 
8.9%
l 488
 
8.5%
c 488
 
8.5%
r 421
 
7.3%
o 383
 
6.6%
P 292
 
5.1%
n 287
 
5.0%
F 223
 
3.9%
h 198
 
3.4%
Other values (8) 1467
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1000
17.4%
i 515
 
8.9%
l 488
 
8.5%
c 488
 
8.5%
r 421
 
7.3%
o 383
 
6.6%
P 292
 
5.1%
n 287
 
5.0%
F 223
 
3.9%
h 198
 
3.4%
Other values (8) 1467
25.5%

incident_state
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
NY
262 
SC
248 
WV
217 
VA
110 
NC
110 
Other values (2)
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSC
2nd rowVA
3rd rowNY
4th rowOH
5th rowNY

Common Values

ValueCountFrequency (%)
NY 262
26.2%
SC 248
24.8%
WV 217
21.7%
VA 110
11.0%
NC 110
11.0%
PA 30
 
3.0%
OH 23
 
2.3%

Length

2023-08-07T20:21:33.495991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:33.585783image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
ny 262
26.2%
sc 248
24.8%
wv 217
21.7%
va 110
11.0%
nc 110
11.0%
pa 30
 
3.0%
oh 23
 
2.3%

Most occurring characters

ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

incident_city
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Springfield
157 
Arlington
152 
Columbus
149 
Northbend
145 
Hillsdale
141 
Other values (2)
256 

Length

Max length11
Median length9
Mean length9.287
Min length8

Characters and Unicode

Total characters9287
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColumbus
2nd rowRiverwood
3rd rowColumbus
4th rowArlington
5th rowArlington

Common Values

ValueCountFrequency (%)
Springfield 157
15.7%
Arlington 152
15.2%
Columbus 149
14.9%
Northbend 145
14.5%
Hillsdale 141
14.1%
Riverwood 134
13.4%
Northbrook 122
12.2%

Length

2023-08-07T20:21:33.677609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:33.777370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
springfield 157
15.7%
arlington 152
15.2%
columbus 149
14.9%
northbend 145
14.5%
hillsdale 141
14.1%
riverwood 134
13.4%
northbrook 122
12.2%

Most occurring characters

ValueCountFrequency (%)
o 1080
 
11.6%
l 881
 
9.5%
r 832
 
9.0%
i 741
 
8.0%
n 606
 
6.5%
d 577
 
6.2%
e 577
 
6.2%
t 419
 
4.5%
b 416
 
4.5%
g 309
 
3.3%
Other values (16) 2849
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8287
89.2%
Uppercase Letter 1000
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1080
13.0%
l 881
10.6%
r 832
10.0%
i 741
8.9%
n 606
 
7.3%
d 577
 
7.0%
e 577
 
7.0%
t 419
 
5.1%
b 416
 
5.0%
g 309
 
3.7%
Other values (10) 1849
22.3%
Uppercase Letter
ValueCountFrequency (%)
N 267
26.7%
S 157
15.7%
A 152
15.2%
C 149
14.9%
H 141
14.1%
R 134
13.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 9287
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1080
 
11.6%
l 881
 
9.5%
r 832
 
9.0%
i 741
 
8.0%
n 606
 
6.5%
d 577
 
6.2%
e 577
 
6.2%
t 419
 
4.5%
b 416
 
4.5%
g 309
 
3.3%
Other values (16) 2849
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1080
 
11.6%
l 881
 
9.5%
r 832
 
9.0%
i 741
 
8.0%
n 606
 
6.5%
d 577
 
6.2%
e 577
 
6.2%
t 419
 
4.5%
b 416
 
4.5%
g 309
 
3.3%
Other values (16) 2849
30.7%

incident_location
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
9935 4th Drive
 
1
4214 MLK Ridge
 
1
8548 Cherokee Ridge
 
1
2352 MLK Drive
 
1
9734 2nd Ridge
 
1
Other values (995)
995 

Length

Max length23
Median length20
Mean length14.749
Min length11

Characters and Unicode

Total characters14749
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row9935 4th Drive
2nd row6608 MLK Hwy
3rd row7121 Francis Lane
4th row6956 Maple Drive
5th row3041 3rd Ave

Common Values

ValueCountFrequency (%)
9935 4th Drive 1
 
0.1%
4214 MLK Ridge 1
 
0.1%
8548 Cherokee Ridge 1
 
0.1%
2352 MLK Drive 1
 
0.1%
9734 2nd Ridge 1
 
0.1%
3122 Apache Drive 1
 
0.1%
9816 Britain St 1
 
0.1%
8214 Flute St 1
 
0.1%
6259 Lincoln Hwy 1
 
0.1%
4492 Andromedia Ave 1
 
0.1%
Other values (990) 990
99.0%

Length

2023-08-07T20:21:33.876778image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drive 173
 
5.8%
lane 171
 
5.7%
ridge 171
 
5.7%
st 171
 
5.7%
ave 161
 
5.4%
hwy 153
 
5.1%
4th 57
 
1.9%
5th 52
 
1.7%
texas 47
 
1.6%
francis 45
 
1.5%
Other values (961) 1799
60.0%

Most occurring characters

ValueCountFrequency (%)
2000
 
13.6%
e 1236
 
8.4%
i 629
 
4.3%
a 603
 
4.1%
n 518
 
3.5%
r 491
 
3.3%
t 474
 
3.2%
5 467
 
3.2%
1 443
 
3.0%
4 441
 
3.0%
Other values (39) 7447
50.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6659
45.1%
Decimal Number 4201
28.5%
Space Separator 2000
 
13.6%
Uppercase Letter 1889
 
12.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1236
18.6%
i 629
9.4%
a 603
 
9.1%
n 518
 
7.8%
r 491
 
7.4%
t 474
 
7.1%
v 377
 
5.7%
d 322
 
4.8%
o 255
 
3.8%
h 212
 
3.2%
Other values (12) 1542
23.2%
Uppercase Letter
ValueCountFrequency (%)
L 258
13.7%
S 237
12.5%
A 230
12.2%
R 205
10.9%
D 173
9.2%
H 153
8.1%
T 89
 
4.7%
F 86
 
4.6%
M 83
 
4.4%
W 80
 
4.2%
Other values (6) 295
15.6%
Decimal Number
ValueCountFrequency (%)
5 467
11.1%
1 443
10.5%
4 441
10.5%
3 441
10.5%
8 437
10.4%
2 430
10.2%
9 428
10.2%
7 425
10.1%
6 404
9.6%
0 285
6.8%
Space Separator
ValueCountFrequency (%)
2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8548
58.0%
Common 6201
42.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1236
 
14.5%
i 629
 
7.4%
a 603
 
7.1%
n 518
 
6.1%
r 491
 
5.7%
t 474
 
5.5%
v 377
 
4.4%
d 322
 
3.8%
L 258
 
3.0%
o 255
 
3.0%
Other values (28) 3385
39.6%
Common
ValueCountFrequency (%)
2000
32.3%
5 467
 
7.5%
1 443
 
7.1%
4 441
 
7.1%
3 441
 
7.1%
8 437
 
7.0%
2 430
 
6.9%
9 428
 
6.9%
7 425
 
6.9%
6 404
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2000
 
13.6%
e 1236
 
8.4%
i 629
 
4.3%
a 603
 
4.1%
n 518
 
3.5%
r 491
 
3.3%
t 474
 
3.2%
5 467
 
3.2%
1 443
 
3.0%
4 441
 
3.0%
Other values (39) 7447
50.5%

incident_hour_of_the_day
Real number (ℝ)

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.644
Minimum0
Maximum23
Zeros52
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:33.953601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median12
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.9513729
Coefficient of variation (CV)0.59699184
Kurtosis-1.1929402
Mean11.644
Median Absolute Deviation (MAD)6
Skewness-0.035584466
Sum11644
Variance48.321586
MonotonicityNot monotonic
2023-08-07T20:21:34.184834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17 54
 
5.4%
3 53
 
5.3%
0 52
 
5.2%
23 51
 
5.1%
16 49
 
4.9%
13 46
 
4.6%
10 46
 
4.6%
4 46
 
4.6%
6 44
 
4.4%
9 43
 
4.3%
Other values (14) 516
51.6%
ValueCountFrequency (%)
0 52
5.2%
1 29
2.9%
2 31
3.1%
3 53
5.3%
4 46
4.6%
5 33
3.3%
6 44
4.4%
7 40
4.0%
8 36
3.6%
9 43
4.3%
ValueCountFrequency (%)
23 51
5.1%
22 38
3.8%
21 42
4.2%
20 34
3.4%
19 40
4.0%
18 41
4.1%
17 54
5.4%
16 49
4.9%
15 39
3.9%
14 43
4.3%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
581 
3
358 
4
 
31
2
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Length

2023-08-07T20:21:34.265331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:34.349951image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

property_damage
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
?
360 
NO
338 
YES
302 

Length

Max length3
Median length2
Mean length1.942
Min length1

Characters and Unicode

Total characters1942
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd row?
3rd rowNO
4th row?
5th rowNO

Common Values

ValueCountFrequency (%)
? 360
36.0%
NO 338
33.8%
YES 302
30.2%

Length

2023-08-07T20:21:34.432122image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:34.526868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
360
36.0%
no 338
33.8%
yes 302
30.2%

Most occurring characters

ValueCountFrequency (%)
? 360
18.5%
N 338
17.4%
O 338
17.4%
Y 302
15.6%
E 302
15.6%
S 302
15.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1582
81.5%
Other Punctuation 360
 
18.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 338
21.4%
O 338
21.4%
Y 302
19.1%
E 302
19.1%
S 302
19.1%
Other Punctuation
ValueCountFrequency (%)
? 360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1582
81.5%
Common 360
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 338
21.4%
O 338
21.4%
Y 302
19.1%
E 302
19.1%
S 302
19.1%
Common
ValueCountFrequency (%)
? 360
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 360
18.5%
N 338
17.4%
O 338
17.4%
Y 302
15.6%
E 302
15.6%
S 302
15.6%

bodily_injuries
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
340 
2
332 
1
328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Length

2023-08-07T20:21:34.600514image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:34.680350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring characters

ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

witnesses
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
258 
2
250 
0
249 
3
243 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Length

2023-08-07T20:21:34.751133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:34.832275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring characters

ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
?
343 
NO
343 
YES
314 

Length

Max length3
Median length2
Mean length1.971
Min length1

Characters and Unicode

Total characters1971
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd row?
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
? 343
34.3%
NO 343
34.3%
YES 314
31.4%

Length

2023-08-07T20:21:34.918853image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T20:21:35.010768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
343
34.3%
no 343
34.3%
yes 314
31.4%

Most occurring characters

ValueCountFrequency (%)
? 343
17.4%
N 343
17.4%
O 343
17.4%
Y 314
15.9%
E 314
15.9%
S 314
15.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1628
82.6%
Other Punctuation 343
 
17.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 343
21.1%
O 343
21.1%
Y 314
19.3%
E 314
19.3%
S 314
19.3%
Other Punctuation
ValueCountFrequency (%)
? 343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1628
82.6%
Common 343
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 343
21.1%
O 343
21.1%
Y 314
19.3%
E 314
19.3%
S 314
19.3%
Common
ValueCountFrequency (%)
? 343
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 343
17.4%
N 343
17.4%
O 343
17.4%
Y 314
15.9%
E 314
15.9%
S 314
15.9%

total_claim_amount
Real number (ℝ)

Distinct763
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52761.94
Minimum100
Maximum114920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:35.090937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4320
Q141812.5
median58055
Q370592.5
95-th percentile88413
Maximum114920
Range114820
Interquartile range (IQR)28780

Descriptive statistics

Standard deviation26401.533
Coefficient of variation (CV)0.50038974
Kurtosis-0.45408143
Mean52761.94
Median Absolute Deviation (MAD)13855
Skewness-0.59458199
Sum52761940
Variance6.9704095 × 108
MonotonicityNot monotonic
2023-08-07T20:21:35.197581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59400 5
 
0.5%
2640 4
 
0.4%
70400 4
 
0.4%
4320 4
 
0.4%
44200 4
 
0.4%
75400 4
 
0.4%
60600 4
 
0.4%
3190 4
 
0.4%
58500 4
 
0.4%
70290 4
 
0.4%
Other values (753) 959
95.9%
ValueCountFrequency (%)
100 1
 
0.1%
1920 1
 
0.1%
2160 1
 
0.1%
2250 1
 
0.1%
2400 1
 
0.1%
2520 1
 
0.1%
2640 4
0.4%
2700 2
0.2%
2800 1
 
0.1%
2860 1
 
0.1%
ValueCountFrequency (%)
114920 1
0.1%
112320 1
0.1%
108480 1
0.1%
108030 1
0.1%
107900 1
0.1%
105820 1
0.1%
105040 1
0.1%
104610 1
0.1%
103560 1
0.1%
101860 1
0.1%

injury_claim
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct638
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7433.42
Minimum0
Maximum21450
Zeros25
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:35.302394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14295
median6775
Q311305
95-th percentile15662
Maximum21450
Range21450
Interquartile range (IQR)7010

Descriptive statistics

Standard deviation4880.9519
Coefficient of variation (CV)0.65662264
Kurtosis-0.76308706
Mean7433.42
Median Absolute Deviation (MAD)3705
Skewness0.26481088
Sum7433420
Variance23823691
MonotonicityNot monotonic
2023-08-07T20:21:35.396537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
2.5%
640 7
 
0.7%
480 7
 
0.7%
660 5
 
0.5%
580 5
 
0.5%
13520 5
 
0.5%
1180 5
 
0.5%
860 5
 
0.5%
6340 5
 
0.5%
780 5
 
0.5%
Other values (628) 926
92.6%
ValueCountFrequency (%)
0 25
2.5%
10 1
 
0.1%
220 1
 
0.1%
250 1
 
0.1%
280 2
 
0.2%
290 1
 
0.1%
300 3
 
0.3%
330 2
 
0.2%
350 1
 
0.1%
360 1
 
0.1%
ValueCountFrequency (%)
21450 1
0.1%
21330 1
0.1%
20700 1
0.1%
19020 1
0.1%
18520 1
0.1%
18220 1
0.1%
18180 1
0.1%
18080 1
0.1%
18000 1
0.1%
17880 1
0.1%

property_claim
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct626
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7399.57
Minimum0
Maximum23670
Zeros19
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:35.490390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14445
median6750
Q310885
95-th percentile15540
Maximum23670
Range23670
Interquartile range (IQR)6440

Descriptive statistics

Standard deviation4824.7262
Coefficient of variation (CV)0.65202791
Kurtosis-0.37638631
Mean7399.57
Median Absolute Deviation (MAD)3290
Skewness0.37816878
Sum7399570
Variance23277983
MonotonicityNot monotonic
2023-08-07T20:21:35.586202image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
1.9%
860 6
 
0.6%
480 5
 
0.5%
660 5
 
0.5%
10000 5
 
0.5%
640 5
 
0.5%
650 5
 
0.5%
11080 5
 
0.5%
840 4
 
0.4%
5310 4
 
0.4%
Other values (616) 937
93.7%
ValueCountFrequency (%)
0 19
1.9%
20 1
 
0.1%
240 1
 
0.1%
250 1
 
0.1%
260 1
 
0.1%
280 3
 
0.3%
290 2
 
0.2%
300 3
 
0.3%
320 3
 
0.3%
330 1
 
0.1%
ValueCountFrequency (%)
23670 1
0.1%
21810 1
0.1%
21630 1
0.1%
21580 1
0.1%
21240 1
0.1%
20550 1
0.1%
20310 1
0.1%
20280 1
0.1%
19950 1
0.1%
19650 1
0.1%

vehicle_claim
Real number (ℝ)

Distinct726
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37928.95
Minimum70
Maximum79560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:35.684937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile3273.5
Q130292.5
median42100
Q350822.5
95-th percentile63094.5
Maximum79560
Range79490
Interquartile range (IQR)20530

Descriptive statistics

Standard deviation18886.253
Coefficient of variation (CV)0.49793767
Kurtosis-0.44657292
Mean37928.95
Median Absolute Deviation (MAD)9840
Skewness-0.62109793
Sum37928950
Variance3.5669055 × 108
MonotonicityNot monotonic
2023-08-07T20:21:35.786729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5040 7
 
0.7%
3360 6
 
0.6%
52080 5
 
0.5%
4720 5
 
0.5%
3600 5
 
0.5%
44800 5
 
0.5%
33600 5
 
0.5%
42720 4
 
0.4%
41580 4
 
0.4%
35000 4
 
0.4%
Other values (716) 950
95.0%
ValueCountFrequency (%)
70 1
0.1%
1440 2
0.2%
1680 2
0.2%
1750 1
0.1%
1760 1
0.1%
1800 1
0.1%
1960 2
0.2%
1980 1
0.1%
2030 1
0.1%
2080 1
0.1%
ValueCountFrequency (%)
79560 1
0.1%
77760 1
0.1%
77670 2
0.2%
76400 1
0.1%
76000 1
0.1%
75600 1
0.1%
75530 1
0.1%
74790 1
0.1%
73620 1
0.1%
73260 1
0.1%

auto_make
Categorical

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Saab
80 
Dodge
80 
Suburu
80 
Nissan
78 
Chevrolet
76 
Other values (9)
606 

Length

Max length10
Median length9
Mean length5.703
Min length3

Characters and Unicode

Total characters5703
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaab
2nd rowMercedes
3rd rowDodge
4th rowChevrolet
5th rowAccura

Common Values

ValueCountFrequency (%)
Saab 80
 
8.0%
Dodge 80
 
8.0%
Suburu 80
 
8.0%
Nissan 78
 
7.8%
Chevrolet 76
 
7.6%
Ford 72
 
7.2%
BMW 72
 
7.2%
Toyota 70
 
7.0%
Audi 69
 
6.9%
Accura 68
 
6.8%
Other values (4) 255
25.5%

Length

2023-08-07T20:21:35.891536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
saab 80
 
8.0%
dodge 80
 
8.0%
suburu 80
 
8.0%
nissan 78
 
7.8%
chevrolet 76
 
7.6%
ford 72
 
7.2%
bmw 72
 
7.2%
toyota 70
 
7.0%
audi 69
 
6.9%
accura 68
 
6.8%
Other values (4) 255
25.5%

Most occurring characters

ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4559
79.9%
Uppercase Letter 1144
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 629
13.8%
a 499
10.9%
o 491
10.8%
u 377
 
8.3%
r 361
 
7.9%
d 341
 
7.5%
s 289
 
6.3%
c 201
 
4.4%
n 201
 
4.4%
b 160
 
3.5%
Other values (10) 1010
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 160
14.0%
M 137
12.0%
A 137
12.0%
D 80
7.0%
N 78
6.8%
C 76
 
6.6%
B 72
 
6.3%
F 72
 
6.3%
W 72
 
6.3%
T 70
 
6.1%
Other values (3) 190
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5703
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

auto_model
Categorical

Distinct39
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
RAM
 
43
Wrangler
 
42
A3
 
37
Neon
 
37
MDX
 
36
Other values (34)
805 

Length

Max length14
Median length9
Mean length5.178
Min length2

Characters and Unicode

Total characters5178
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row92x
2nd rowE400
3rd rowRAM
4th rowTahoe
5th rowRSX

Common Values

ValueCountFrequency (%)
RAM 43
 
4.3%
Wrangler 42
 
4.2%
A3 37
 
3.7%
Neon 37
 
3.7%
MDX 36
 
3.6%
Jetta 35
 
3.5%
Passat 33
 
3.3%
A5 32
 
3.2%
Legacy 32
 
3.2%
Pathfinder 31
 
3.1%
Other values (29) 642
64.2%

Length

2023-08-07T20:21:35.989297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ram 43
 
4.1%
wrangler 42
 
4.0%
a3 37
 
3.5%
neon 37
 
3.5%
mdx 36
 
3.5%
jetta 35
 
3.4%
passat 33
 
3.2%
a5 32
 
3.1%
legacy 32
 
3.1%
pathfinder 31
 
3.0%
Other values (31) 685
65.7%

Most occurring characters

ValueCountFrequency (%)
a 492
 
9.5%
e 428
 
8.3%
r 392
 
7.6%
o 238
 
4.6%
i 235
 
4.5%
t 185
 
3.6%
l 179
 
3.5%
n 178
 
3.4%
M 168
 
3.2%
s 157
 
3.0%
Other values (42) 2526
48.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3351
64.7%
Uppercase Letter 1207
 
23.3%
Decimal Number 577
 
11.1%
Space Separator 43
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 492
14.7%
e 428
12.8%
r 392
11.7%
o 238
 
7.1%
i 235
 
7.0%
t 185
 
5.5%
l 179
 
5.3%
n 178
 
5.3%
s 157
 
4.7%
d 113
 
3.4%
Other values (13) 754
22.5%
Uppercase Letter
ValueCountFrequency (%)
M 168
13.9%
C 133
11.0%
A 125
10.4%
X 87
 
7.2%
F 76
 
6.3%
R 75
 
6.2%
L 72
 
6.0%
P 64
 
5.3%
S 52
 
4.3%
E 51
 
4.2%
Other values (10) 304
25.2%
Decimal Number
ValueCountFrequency (%)
5 144
25.0%
0 137
23.7%
3 118
20.5%
9 80
13.9%
2 28
 
4.9%
4 27
 
4.7%
1 27
 
4.7%
6 16
 
2.8%
Space Separator
ValueCountFrequency (%)
43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4558
88.0%
Common 620
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 492
 
10.8%
e 428
 
9.4%
r 392
 
8.6%
o 238
 
5.2%
i 235
 
5.2%
t 185
 
4.1%
l 179
 
3.9%
n 178
 
3.9%
M 168
 
3.7%
s 157
 
3.4%
Other values (33) 1906
41.8%
Common
ValueCountFrequency (%)
5 144
23.2%
0 137
22.1%
3 118
19.0%
9 80
12.9%
43
 
6.9%
2 28
 
4.5%
4 27
 
4.4%
1 27
 
4.4%
6 16
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 492
 
9.5%
e 428
 
8.3%
r 392
 
7.6%
o 238
 
4.6%
i 235
 
4.5%
t 185
 
3.6%
l 179
 
3.5%
n 178
 
3.4%
M 168
 
3.2%
s 157
 
3.0%
Other values (42) 2526
48.8%

auto_year
Real number (ℝ)

Distinct21
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.103
Minimum1995
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-07T20:21:36.077115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1995
Q12000
median2005
Q32010
95-th percentile2014
Maximum2015
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0158608
Coefficient of variation (CV)0.0030002752
Kurtosis-1.1718678
Mean2005.103
Median Absolute Deviation (MAD)5
Skewness-0.048288807
Sum2005103
Variance36.190582
MonotonicityNot monotonic
2023-08-07T20:21:36.162981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1995 56
 
5.6%
1999 55
 
5.5%
2005 54
 
5.4%
2006 53
 
5.3%
2011 53
 
5.3%
2007 52
 
5.2%
2003 51
 
5.1%
2009 50
 
5.0%
2010 50
 
5.0%
2013 49
 
4.9%
Other values (11) 477
47.7%
ValueCountFrequency (%)
1995 56
5.6%
1996 37
3.7%
1997 46
4.6%
1998 40
4.0%
1999 55
5.5%
2000 42
4.2%
2001 42
4.2%
2002 49
4.9%
2003 51
5.1%
2004 39
3.9%
ValueCountFrequency (%)
2015 47
4.7%
2014 44
4.4%
2013 49
4.9%
2012 46
4.6%
2011 53
5.3%
2010 50
5.0%
2009 50
5.0%
2008 45
4.5%
2007 52
5.2%
2006 53
5.3%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
753 
True
247 
ValueCountFrequency (%)
False 753
75.3%
True 247
 
24.7%
2023-08-07T20:21:36.260973image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

_c39
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1000
Missing (%)100.0%
Memory size7.9 KiB

Interactions

2023-08-07T20:21:27.614802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:10.297000image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.607662image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:12.954529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.218686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.603654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.889160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.347466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.599720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.893711image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:22.294775image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.604040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.871449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.128965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:27.711604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:10.416667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.694744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.041039image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.318942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.692470image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.982940image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.436375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.690135image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.980444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:22.384342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.692554image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.961022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.219633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:27.803247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:10.502184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.775511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.124550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.411076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.776421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:17.072093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.519927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.778080image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.067185image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:22.472857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.777068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.045557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-08-07T20:21:27.895928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:10.589871image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.862249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.207877image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.506846image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.869992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:17.162558image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.605871image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.861929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.151016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:22.563367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.865634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.133080image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.403418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:27.996725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:10.688114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.957404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.303746image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.610628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.979149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:17.386357image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.703808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.964059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.250749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:22.664878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.962314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.229604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.506302image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:28.095504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:10.775898image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-08-07T20:21:13.391773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.709276image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.065938image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:17.483222image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.790479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.053409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.339545image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:22.757393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.049833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.317731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.599892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:28.199596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:10.878618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:12.155396image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.490506image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.813038image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.159699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:17.579026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.886271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.155334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.437334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:22.860907image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.149344image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.414896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.706752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:28.295502image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:10.966428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:12.240907image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.584237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.925025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.248431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:17.679440image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.970914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.244127image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.524913image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:22.952878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.234871image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.502212image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.801221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:28.388875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.061370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:12.426241image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.668961image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.019692image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.334035image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:17.772232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.057257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.332845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.615946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.046390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.319895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.591008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.895737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:28.479681image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.143946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:12.505749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.757236image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.109669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.422816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:17.861044image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.138983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.420770image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.697865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.135969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.410146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.675848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.988251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:28.581488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.237005image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:12.592754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.848000image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.207285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.514853image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:17.964114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.229694image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.514559image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.790384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.227495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.501655image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.764216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:27.085768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:28.669228image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.320795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:12.679323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:13.930800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.297422image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.599868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.051723image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.316460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.602602image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.875899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.312499image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.586171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.846216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:27.177282image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:28.762016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.408622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:12.764201image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.012133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.389147image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.692295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.142089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.401476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.691397image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:21.961411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.403016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.668683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:25.930013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:27.407182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:28.865421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:11.506358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:12.855709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:14.117017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:15.494778image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:16.789380image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:18.243046image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:19.499995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:20.791231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:22.198432image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:23.500524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:24.772916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:26.030769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-07T20:21:27.511006image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-08-07T20:21:36.362932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
months_as_customeragepolicy_numberpolicy_annual_premiumumbrella_limitinsured_zipcapital-gainscapital-lossincident_hour_of_the_daytotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_yearpolicy_statepolicy_cslpolicy_deductableinsured_sexinsured_education_levelinsured_occupationinsured_hobbiesinsured_relationshipincident_dateincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_citynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availableauto_makeauto_modelfraud_reported
months_as_customer1.0000.9130.0580.0200.0050.013-0.0050.0140.0760.0530.0640.0250.0480.0040.0170.0000.0000.0340.0000.0000.0290.0640.0000.0000.0320.0540.0000.0000.0290.0000.0000.0440.0000.0920.0000.0000.000
age0.9131.0000.0610.0310.0020.009-0.0210.0010.0950.0650.0740.0560.0510.0060.0000.0000.0000.0800.0000.0000.0000.0370.0000.0000.0660.0530.0000.0000.0000.0810.0000.0000.0460.0000.0000.0000.000
policy_number0.0580.0611.0000.0180.005-0.0010.007-0.008-0.001-0.008-0.011-0.003-0.013-0.0010.0580.0000.0000.0000.0000.0140.0480.0520.0600.0000.0460.0000.0030.0000.0260.0000.0560.0000.0410.0000.0260.0000.000
policy_annual_premium0.0200.0310.0181.000-0.0010.043-0.0150.032-0.003-0.002-0.019-0.0040.007-0.0300.0000.0790.0520.1190.0000.0350.0000.0000.0710.0000.0000.0000.0070.0000.0000.0000.0560.0000.0360.0340.0000.0000.013
umbrella_limit0.0050.0020.005-0.0011.0000.004-0.043-0.021-0.021-0.041-0.047-0.018-0.0380.0120.0000.0000.0000.0000.0000.0270.0000.0430.0000.0000.0000.0540.0000.0210.0680.0300.0430.0680.0000.0680.0060.0300.042
insured_zip0.0130.009-0.0010.0430.0041.0000.0150.0420.010-0.003-0.008-0.015-0.016-0.0280.0140.0000.0000.0400.0000.0770.0120.0000.0390.0180.0000.0220.0000.0270.0310.0330.0370.0000.0000.0680.0000.0000.067
capital-gains-0.005-0.0210.007-0.015-0.0430.0151.000-0.041-0.0160.0120.0230.0050.0100.0270.0000.0000.0000.0000.0490.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0530.0000.0000.0000.0000.000
capital-loss0.0140.001-0.0080.032-0.0210.042-0.0411.000-0.029-0.042-0.046-0.023-0.040-0.0550.0000.0000.0000.0280.0280.0000.0070.0410.0000.0000.0380.0000.0280.0000.0000.0000.0580.0000.0000.0420.0130.0000.000
incident_hour_of_the_day0.0760.095-0.001-0.003-0.0210.010-0.016-0.0291.0000.1780.1660.1690.1740.0200.0290.0000.0740.0000.0000.0000.0000.0000.0000.2640.2760.1950.1750.0350.0000.1140.0000.0240.0540.0000.0000.0000.109
total_claim_amount0.0530.065-0.008-0.002-0.041-0.0030.012-0.0420.1781.0000.7920.7980.965-0.0330.0240.0000.0460.0370.0540.0360.0150.0000.0000.5760.5770.4280.3840.0000.0000.2340.0000.0000.0000.0730.0000.0140.158
injury_claim0.0640.074-0.011-0.019-0.047-0.0080.023-0.0460.1660.7921.0000.5690.684-0.0190.0610.0000.0530.0000.0000.0330.0370.0420.0230.5330.5330.3900.3620.0000.0000.2010.0000.0000.0000.0330.0000.0000.114
property_claim0.0250.056-0.003-0.004-0.018-0.0150.005-0.0230.1690.7980.5691.0000.693-0.0080.0000.0000.0630.0250.0000.0000.0340.0000.0000.5400.5440.3970.3620.0000.0190.2050.0270.0000.0000.0000.0300.1010.165
vehicle_claim0.0480.051-0.0130.007-0.038-0.0160.010-0.0400.1740.9650.6840.6931.000-0.0410.0000.0280.0000.0000.0450.0540.0360.0000.0000.5770.5750.4270.3790.0340.0000.2340.0000.0000.0000.0650.0000.0000.169
auto_year0.0040.006-0.001-0.0300.012-0.0280.027-0.0550.020-0.033-0.019-0.008-0.0411.0000.0310.0000.0000.0440.0000.0380.0000.0600.0390.0480.0380.0000.0660.0490.0000.0330.0000.0440.0180.0470.0000.0000.000
policy_state0.0170.0000.0580.0000.0000.0140.0000.0000.0290.0240.0610.0000.0000.0311.0000.0000.0000.0000.0360.0000.0410.0000.0000.0000.0230.0000.0000.0000.0230.0000.0370.0470.0000.0380.0500.0500.000
policy_csl0.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0001.0000.0000.0600.0640.0510.0000.0380.0000.0270.0470.0070.0430.0000.0000.0000.0000.0000.0200.0330.0000.0000.012
policy_deductable0.0000.0000.0000.0520.0000.0000.0000.0000.0740.0460.0530.0630.0000.0000.0000.0001.0000.0000.0000.0810.0000.0000.0410.0000.0000.0000.0000.0210.0000.0480.0000.0180.0430.0000.0000.0000.000
insured_sex0.0340.0800.0000.1190.0000.0400.0000.0280.0000.0370.0000.0250.0000.0440.0000.0600.0001.0000.0000.0000.0000.0000.0780.0000.0000.0000.0530.0640.0100.0000.0000.0000.0000.0000.0000.0000.000
insured_education_level0.0000.0000.0000.0000.0000.0000.0490.0280.0000.0540.0000.0000.0450.0000.0360.0640.0000.0001.0000.0410.0000.0420.0000.0290.0560.0000.0000.0440.0000.0200.0210.0090.0600.0500.0380.0280.000
insured_occupation0.0000.0000.0140.0350.0270.0770.0000.0000.0000.0360.0330.0000.0540.0380.0000.0510.0810.0000.0411.0000.0500.0540.0530.0110.0000.0000.0000.0290.0000.0000.0000.0940.0310.0000.0000.0370.068
insured_hobbies0.0290.0000.0480.0000.0000.0120.0610.0070.0000.0150.0370.0340.0360.0000.0410.0000.0000.0000.0000.0501.0000.0270.0510.0380.0490.0000.0000.0690.0290.0000.0000.0000.0340.0940.0470.0510.379
insured_relationship0.0640.0370.0520.0000.0430.0000.0000.0410.0000.0000.0420.0000.0000.0600.0000.0380.0000.0000.0420.0540.0271.0000.0370.0000.0000.0000.0260.0290.0000.0000.0000.0000.0000.0000.0120.0230.020
incident_date0.0000.0000.0600.0710.0000.0390.0000.0000.0000.0000.0230.0000.0000.0390.0000.0000.0410.0780.0000.0530.0510.0371.0000.0000.0000.0000.0000.0000.0000.0440.0580.0000.0690.0000.1170.0780.061
incident_type0.0000.0000.0000.0000.0000.0180.0000.0000.2640.5760.5330.5400.5770.0480.0000.0270.0000.0000.0290.0110.0380.0000.0001.0000.5770.4250.4430.0240.0310.5760.0000.0100.0000.0000.0000.0890.162
collision_type0.0320.0660.0460.0000.0000.0000.0000.0380.2760.5770.5330.5440.5750.0380.0230.0470.0000.0000.0560.0000.0490.0000.0000.5771.0000.4250.4400.0610.0300.2280.0000.0000.0530.0130.0000.0000.168
incident_severity0.0540.0530.0000.0000.0540.0220.0000.0000.1950.4280.3900.3970.4270.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.4250.4251.0000.3120.0290.0000.1710.0620.0000.0200.0100.0000.0310.511
authorities_contacted0.0000.0000.0030.0070.0000.0000.0000.0280.1750.3840.3620.3620.3790.0660.0000.0430.0000.0530.0000.0000.0000.0260.0000.4430.4400.3121.0000.0000.0460.1670.0000.0000.0000.0000.0000.0000.149
incident_state0.0000.0000.0000.0000.0210.0270.0000.0000.0350.0000.0000.0000.0340.0490.0000.0000.0210.0640.0440.0290.0690.0290.0000.0240.0610.0290.0001.0000.0000.0270.0000.0000.0300.0270.0270.0140.101
incident_city0.0290.0000.0260.0000.0680.0310.0000.0000.0000.0000.0000.0190.0000.0000.0230.0000.0000.0100.0000.0000.0290.0000.0000.0310.0300.0000.0460.0001.0000.0000.0650.0000.0530.0000.0000.0640.000
number_of_vehicles_involved0.0000.0810.0000.0000.0300.0330.0770.0000.1140.2340.2010.2050.2340.0330.0000.0000.0480.0000.0200.0000.0000.0000.0440.5760.2280.1710.1670.0270.0001.0000.0000.0000.0000.0000.0410.0870.030
property_damage0.0000.0000.0560.0560.0430.0370.0000.0580.0000.0000.0000.0270.0000.0000.0370.0000.0000.0000.0210.0000.0000.0000.0580.0000.0000.0620.0000.0000.0650.0001.0000.0170.0000.0260.0000.0000.078
bodily_injuries0.0440.0000.0000.0000.0680.0000.0530.0000.0240.0000.0000.0000.0000.0440.0470.0000.0180.0000.0090.0940.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0171.0000.0170.0000.0000.0000.000
witnesses0.0000.0460.0410.0360.0000.0000.0000.0000.0540.0000.0000.0000.0000.0180.0000.0200.0430.0000.0600.0310.0340.0000.0690.0000.0530.0200.0000.0300.0530.0000.0000.0171.0000.0000.0000.0000.056
police_report_available0.0920.0000.0000.0340.0680.0680.0000.0420.0000.0730.0330.0000.0650.0470.0380.0330.0000.0000.0500.0000.0940.0000.0000.0000.0130.0100.0000.0270.0000.0000.0260.0000.0001.0000.0000.0350.000
auto_make0.0000.0000.0260.0000.0060.0000.0000.0130.0000.0000.0000.0300.0000.0000.0500.0000.0000.0000.0380.0000.0470.0120.1170.0000.0000.0000.0000.0270.0000.0410.0000.0000.0000.0001.0000.9870.028
auto_model0.0000.0000.0000.0000.0300.0000.0000.0000.0000.0140.0000.1010.0000.0000.0500.0000.0000.0000.0280.0370.0510.0230.0780.0890.0000.0310.0000.0140.0640.0870.0000.0000.0000.0350.9871.0000.093
fraud_reported0.0000.0000.0000.0130.0420.0670.0000.0000.1090.1580.1140.1650.1690.0000.0000.0120.0000.0000.0000.0680.3790.0200.0610.1620.1680.5110.1490.1010.0000.0300.0780.0000.0560.0000.0280.0931.000

Missing values

2023-08-07T20:21:29.062985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-07T20:21:29.469779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

months_as_customeragepolicy_numberpolicy_bind_datepolicy_statepolicy_cslpolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_hobbiesinsured_relationshipcapital-gainscapital-lossincident_dateincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_cityincident_locationincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_modelauto_yearfraud_reported_c39
0328485215852014-10-17OH250/50010001406.910466132MALEMDcraft-repairsleepinghusband5330002015-01-25Single Vehicle CollisionSide CollisionMajor DamagePoliceSCColumbus9935 4th Drive51YES12YES7161065101302052080Saab92x2004YNaN
1228423428682006-06-27IN250/50020001197.225000000468176MALEMDmachine-op-inspctreadingother-relative002015-01-21Vehicle Theft?Minor DamagePoliceVARiverwood6608 MLK Hwy81?00?50707807803510MercedesE4002007YNaN
2134296876982000-09-06OH100/30020001413.145000000430632FEMALEPhDsalesboard-gamesown-child3510002015-02-22Multi-vehicle CollisionRear CollisionMinor DamagePoliceNYColumbus7121 Francis Lane73NO23NO346507700385023100DodgeRAM2007NNaN
3256412278111990-05-25IL250/50020001415.746000000608117FEMALEPhDarmed-forcesboard-gamesunmarried48900-624002015-01-10Single Vehicle CollisionFront CollisionMajor DamagePoliceOHArlington6956 Maple Drive51?12NO634006340634050720ChevroletTahoe2014YNaN
4228443674552014-06-06IL500/100010001583.916000000610706MALEAssociatesalesboard-gamesunmarried66000-460002015-02-17Vehicle Theft?Minor DamageNoneNYArlington3041 3rd Ave201NO01NO650013006504550AccuraRSX2009NNaN
5256391045942006-10-12OH250/50010001351.100478456FEMALEPhDtech-supportbungie-jumpingunmarried002015-01-02Multi-vehicle CollisionRear CollisionMajor DamageFireSCArlington8973 Washington St193NO02NO641006410641051280Saab952003YNaN
6137344139782000-06-04IN250/50010001333.350441716MALEPhDprof-specialtyboard-gameshusband0-770002015-01-13Multi-vehicle CollisionFront CollisionMinor DamagePoliceNYSpringfield5846 Weaver Drive03?00?7865021450715050050NissanPathfinder2012NNaN
7165374290271990-02-03IL100/30010001137.030603195MALEAssociatetech-supportbase-jumpingunmarried002015-02-27Multi-vehicle CollisionFront CollisionTotal LossPoliceVAColumbus3525 3rd Hwy233?22YES515909380938032830AudiA52015NNaN
827334856651997-02-05IL100/3005001442.990601734FEMALEPhDother-servicegolfown-child002015-01-30Single Vehicle CollisionFront CollisionTotal LossPoliceWVArlington4872 Rock Ridge211NO11YES277002770277022160ToyotaCamry2012NNaN
9212426365502011-07-25IL100/3005001315.680600983MALEPhDpriv-house-servcampingwife0-393002015-01-05Single Vehicle CollisionRear CollisionTotal LossOtherNCHillsdale3066 Francis Ave141NO21?423004700470032900Saab92x1996NNaN
months_as_customeragepolicy_numberpolicy_bind_datepolicy_statepolicy_cslpolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_hobbiesinsured_relationshipcapital-gainscapital-lossincident_dateincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_cityincident_locationincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_modelauto_yearfraud_reported_c39
990286436631901994-02-05IL100/3005001564.433000000477644FEMALEMDprof-specialtymoviesunmarried77500-328002015-01-31Single Vehicle CollisionRear CollisionMinor DamageFireNYNorthbrook4755 1st St181?22YES342903810381026670JeepGrand Cherokee2013NNaN
991257441093922006-07-12OH100/30010001280.880433981MALEMDother-servicebasketballother-relative59400-322002015-02-06Single Vehicle CollisionRear CollisionTotal LossOtherWVRiverwood5312 Francis Ridge211NO01NO469800522041760AccuraTL2002NNaN
99294262152782007-10-24IN100/300500722.660433696MALEMDexec-managerialcampinghusband5030002015-01-23Multi-vehicle CollisionFront CollisionMajor DamageFireOHSpringfield1705 Weaver St63YES12YES367003670734025690NissanPathfinder2010NNaN
993124286745702001-12-08OH250/50010001235.140443567MALEMDexec-managerialcampinghusband0-321002015-02-17Multi-vehicle CollisionSide CollisionTotal LossOtherOHHillsdale1643 Washington Hwy203?01?602006020602048160VolkswagenPassat2012NNaN
994141306814862007-03-24IN500/100010001347.040430665MALEHigh Schoolsalesbungie-jumpingown-child0-821002015-01-22Parked Car?Minor DamageNoneSCNorthbend6516 Solo Drive61?12YES648054010804860HondaCivic1996NNaN
9953389418511991-07-16OH500/100010001310.800431289FEMALEMasterscraft-repairpaintballunmarried002015-02-22Single Vehicle CollisionFront CollisionMinor DamageFireNCNorthbrook6045 Andromedia St201YES01?8720017440872061040HondaAccord2006NNaN
996285411869342014-01-05IL100/30010001436.790608177FEMALEPhDprof-specialtysleepingwife7090002015-01-24Single Vehicle CollisionRear CollisionMajor DamageFireSCNorthbend3092 Texas Drive231YES23?108480180801808072320VolkswagenPassat2015NNaN
997130349185162003-02-17OH250/5005001383.493000000442797FEMALEMastersarmed-forcesbungie-jumpingother-relative3510002015-01-23Multi-vehicle CollisionSide CollisionMinor DamagePoliceNCArlington7629 5th St43?23YES675007500750052500SuburuImpreza1996NNaN
998458625339402011-11-18IL500/100020001356.925000000441714MALEAssociatehandlers-cleanersbase-jumpingwife002015-02-26Single Vehicle CollisionRear CollisionMajor DamageOtherNYArlington6128 Elm Lane21?01YES469805220522036540AudiA51998NNaN
999456605560801996-11-11OH250/5001000766.190612260FEMALEAssociatesaleskayakinghusband002015-02-26Parked Car?Minor DamagePoliceWVColumbus1416 Cherokee Ridge61?03?50604609203680MercedesE4002007NNaN